Topic: sample generation

Learning to generate new samples from an unknown Ising distribution exhibits a computational threshold determined by the interaction matrix's spectral width. Below the threshold, empirically initialized Glauber dynamics gives an efficient learner; just above it, a constructed family is cryptographically hard even with parameter access. The talk distinguishes sample generation from parameter estimation and studies a resulting memorization–hallucination trade-off.

Humans and animals learn and plan with flexibility and efficiency well beyond that of modern Machine Learning methods. This is hypothesized to owe in part to the ability of animals to build structured representations of their environments, and modulate these representations to rapidly adapt to new settings. In the first part of this talk, I will discuss theoretical work describing how learned representations in hippocampus enable rapid adaptation to new goals by learning predictive representations, while entorhinal cortex compresses these predictive representations with spectral methods that s